Timezone: »

 
Poster
Communication-Optimal Distributed Clustering
Jiecao Chen · He Sun · David Woodruff · Qin Zhang

Mon Dec 05 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #2
in Monday Posters »

Clustering large datasets is a fundamental problem with a number of applications in machine learning. Data is often collected on different sites and clustering needs to be performed in a distributed manner with low communication. We would like the quality of the clustering in the distributed setting to match that in the centralized setting for which all the data resides on a single site. In this work, we study both graph and geometric clustering problems in two distributed models: (1) a point-to-point model, and (2) a model with a broadcast channel. We give protocols in both models which we show are nearly optimal by proving almost matching communication lower bounds. Our work highlights the surprising power of a broadcast channel for clustering problems; roughly speaking, to cluster n points or n vertices in a graph distributed across s servers, for a worst-case partitioning the communication complexity in a point-to-point model is n*s, while in the broadcast model it is n + s. We implement our algorithms and demonstrate this phenomenon on real life datasets, showing that our algorithms are also very efficient in practice.

Keywords: Clustering · Spectral Methods · Information Theory · (Other) Unsupervised Learning Methods

Author Information

Jiecao Chen (Indiana University Bloomington)
He Sun (The University of Bristol)
David Woodruff (IBM Research)
Qin Zhang (Indiana University Bloomington)

More from the Same Authors